Septal Curvature Is Marker of Hemodynamic, Anatomical, and Electromechanical Ventricular Interdependence in Patients with Pulmonary Arterial Hypertension
Bibliographic record
Abstract
AIMS: The objective of this study was to determine the factors independently associated with septal curvature in patients with pulmonary arterial hypertension (PAH). METHODS: Eighty-five consecutive patients with PAH who had an echocardiogram and a right heart catheterization within 24 hours of each others were included in the study. Septal curvature was assessed at the mid-papillary level using the eccentricity index (EI). Marked early systolic septal anterior motion was defined as a change in EI > 0.2 between end-diastole and early systole. Inter-ventricular mechanical delay was calculated as the percent time difference between right ventricular (RV) to left ventricular (LV) end-ejection time normalized for the RR interval. RESULTS: Average age was 45 ± 11 years and the majority of patients were women (75%). Mean right atrial pressure was 11 ± 7 mmHg, mean PAP was 52 ± 13 mmHg, relative RV area 1.8 ± 0.9, and RV fractional area change 24 ± 8%. End-diastolic EI was 1.6 ± 0.4 and systolic EI was 2.5 ± 0.8. On multivariate analysis relative pulmonary pressure, relative RV area, and inter-ventricular mechanical delay were independently associated with systolic EI (R(2) = 0.72, P < 0.001). Independent determinants of diastolic EI included relative RV area and mean PAP (R(2) = 0.69, P < 0.001). A systolic EI >1.08 differentiated patients with PAH from healthy controls with an AUC = 0.99. Patients with early systolic septal anterior motion (44% of subjects) had lower exercise capacity, more extensive ventricular remodeling, and worst ventricular function. CONCLUSION: Septal curvature is a useful marker of structural, hemodynamic, and electromechanical ventricular interdependence in PAH.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".